将 sklearn RFE 与来自另一个包的估算器一起使用 [英] Using sklearn RFE with an estimator from another package

问题描述

是否可以将 sklearn 递归特征消除(RFE)与来自另一个包的估计器一起使用?

Is it possible to use sklearn Recursive Feature Elimination(RFE) with an estimator from another package?

具体来说，我想使用 statsmodels 包中的 GLM 并将其包装在 sklearn RFE 中?

Specifically, I want to use GLM from statsmodels package and wrap it in sklearn RFE?

如果有，请举例说明?

推荐答案

是的，这是可能的.您只需要创建一个继承 sklearn.base.BaseEstimator 的类，确保它具有 fit &predict 方法，并确保其 fit 方法通过 coef_ 或 feature_importances_ 属性公开特征重要性.这是一个类的简化示例:

Yes, it is possible. You just need to create a class that inherit sklearn.base.BaseEstimator, make sure it has fit & predict methods, and make sure its fit method expose feature importance through either coef_ or feature_importances_ attribute. Here is a simplified example of a class:

import numpy as np
from sklearn.datasets import make_classification
from sklearn.base import BaseEstimator
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE

class MyEstimator(BaseEstimator):
  def __init__(self):
    self.model = LogisticRegression()

  def fit(self, X, y, **kwargs):
    self.model.fit(X, y)
    self.coef_ = self.model.coef_

  def predict(self, X):
    result = self.model.predict(X)    
    return np.array(result)

if __name__ == '__main__':
  X, y = make_classification(n_features=10, n_redundant=0, n_informative=7, n_clusters_per_class=1)
  estimator = MyEstimator()
  selector = RFE(estimator, 5, step=1)
  selector = selector.fit(X, y)
  print(selector.support_)
  print(selector.ranking_)

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